Determinants of trust in times of crises: A cross-sectional study of 3,065 German-speaking adults from the D-A-CH region.

Interpersonal trust declined worldwide during the COVID-19 pandemic; strategies are needed to restore it. We surveyed 3,065 quota-sampled German-speaking adults residing in the D-A-CH region. Using multinomial logistic regression models and backward elimination for variable selection, we calculated multivariable-adjusted odds ratios (OR) and 95% confidence intervals (95% CIs) to appraise correlates of interpersonal trust using the Interpersonal Trust Short Scale (KUSIV3). Participants with high levels of interpersonal trust (top KUSIV3 tertile (T3)) tended to be older, male, residents of Switzerland, university degree holders, and workers with higher income and work satisfaction (all Pdiff<0.01) compared to those in the lowest KUSIV3 tertile (T1). Optimism was most strongly associated with high interpersonal trust (ORT3vsT1 = 5.75, 95%CI = 4.33-7.64). Also significantly associated with high interpersonal trust were: Having voted in the last national election (for the opposition, OR = 1.39, 95%CI = 1.02-1.89 or the governing party, OR = 1.61, 95%CI = 1.23-2.11) versus non-voters; perspective taking (ORT3vsT1 = 1.46, 95%CI = 1.11-1.91); being more extraverted (ORT3vsT1 = 1.99, 95%CI = 1.53-2.59) and more agreeable (ORT3vsT1 = 1.95, 95% CI = 1.46-2.61); and scoring higher on complexity thinking (ORT3vsT1 = 1.32, 95%CI = 1.01-1.72). Participants scoring significantly lower for interpersonal trust did not regularly participate in religious meetings (OR = 0.61, 95%CI = 0.44-0.84, versus participation at least monthly); were more conscientious (ORT3vsT1 = 0.68, 95%CI = 0.51-0.91) or current smokers (OR = 0.68; 95%CI = 0.53-0.87, versus never smoking); had sleep problems >5 times a week (OR = 0.48; 95%CI = 0.36-0.66, versus none); and scored high on conspiracy belief (ORT3vsT1 = 0.53; 95%CI = 0.41-0.69). Results differed minimally by gender and country. These findings may be helpful in devising targeted strategies to strengthen interpersonal trust and social engagement in European societies, especially during times of crises.

Willingness to receive an annual COVID-19 booster vaccine in the German-speaking D-A-CH region in Europe: A cross-sectional study.

Background: Emergence of new coronavirus variants and waning immunity may necessitate regular COVID-19 vaccine boosters, but empirical data on population willingness for regular vaccination are limited. Methods: In August 2021, we surveyed 3,067 quota-sampled German-speaking adults residing in the D-A-CH region (Germany, Austria, Switzerland). Using multivariable adjusted ordered logistic regression models we calculated odds ratios (OR) and 95% confidence intervals (95% CIs) to assess factors associated with willingness to vaccinate annually against COVID-19. Findings: Among 2,480 participants vaccinated or planning to get vaccinated, 82·4% indicated willingness to receive annual COVID-19 boosters. This willingness was higher in Austria (OR=1·47, 95% CI, 1·19-1·82; p < 0·001) and Germany (OR=1·98, 95% CI, 1·60-2·45; p < 0·001) versus Switzerland and increased with age. Having voted in the last national election (ORopposition party voters=1·51, 95% CI=1·18-1·92; p = 0·001 and ORgoverning party voters=1·57, 95% CI=1·28-1·93; p < 0·001, versus non-voters) and not regularly participating in religious meetings (OR=1·37, 95% CI=1·08-1·73; p = 0·009, versus participation at least monthly) were significantly associated with willingness to vaccinate, as was partial (OR=1·97, 95% CI=1·43-2·72; p < 0·001) or total (OR=5·20, 95% CI=3·76-7·19; p < 0·001) approval of COVID-19 mitigation measures (versus non-approval). By country, Austrians showed the strongest association of voting behavior and mitigation measure approval with willingness to vaccinate. Interpretation: Targeted promotion programs informed by political and religious engagement and mitigation measure approval are needed to increase willingness to receive regular COVID-19 boosters. Funding: Medical University of Vienna, Department of Epidemiology, Danube University Krems, Department for Knowledge and Communication Management; Austrian Society of Epidemiology.

Correlates of COVID-19 vaccine hesitancy in Austria: trust and the government.

BACKGROUND: With the coronavirus disease 2019 (COVID-19) pandemic surging and new mutations evolving, trust in vaccines is essential. METHODS: We explored correlates of vaccine hesitancy, considering political believes and psychosocial concepts, conducting a non-probability quota-sampled online survey with 1007 Austrians. RESULTS: We identified several important correlates of vaccine hesitancy, ranging from demographics to complex factors such as voting behavior or trust in the government. Among those with hesitancy towards a COVID-19 vaccine, having voted for opposition parties (opp) or not voted (novote) were (95% Confidence Intervall (CI)opp, 1.44-2.95) to 2.25-times (95%CInovote, 1.53-3.30) that of having voted for governing parties. Only 46.2% trusted the Austrian government to provide safe vaccines, and 80.7% requested independent scientific evaluations regarding vaccine safety to increase willingness to vaccine. CONCLUSIONS: Contrary to expected, psychosocial dimensions were only weakly correlated with vaccine hesitancy. However, the strong correlation between distrust in the vaccine and distrust in authorities suggests a common cause of disengagement from public discourse.

Innovations are disproportionately likely in the periphery of a scientific network.

The origins of innovation in science are typically understood using historical narratives that tend to be focused on small sets of influential authors, an approach that is rigorous but limited in scope. Here, we develop a framework for rigorously identifying innovation across an entire scientific field through automated analysis of a corpus of over 6000 documents that includes every paper published in the field of evolutionary medicine. This comprehensive approach allows us to explore statistical properties of innovation, asking where innovative ideas tend to originate within a field's pre-existing conceptual framework. First, we develop a measure of innovation based on novelty and persistence, quantifying the collective acceptance of novel language and ideas. Second, we study the field's conceptual landscape through a bibliographic coupling network. We find that innovations are disproportionately more likely in the periphery of the bibliographic coupling network, suggesting that the relative freedom allowed by remaining unconnected with well-established lines of research could be beneficial to creating novel and lasting change. In this way, the emergence of collective computation in scientific disciplines may have robustness-adaptability trade-offs that are similar to those found in other biosocial complex systems.

Systematic shifts in scaling behavior based on organizational strategy in universities.

To build better theories of cities, companies, and other social institutions such as universities, requires that we understand the tradeoffs and complementarities that exist between their core functions, and that we understand bounds to their growth. Scaling theory has been a powerful tool for addressing such questions in diverse physical, biological and urban systems, revealing systematic quantitative regularities between size and function. Here we apply scaling theory to the social sciences, taking a synoptic view of an entire class of institutions. The United States higher education system serves as an ideal case study, since it includes over 5,800 institutions with shared broad objectives, but ranges in strategy from vocational training to the production of novel research, contains public, nonprofit and for-profit models, and spans sizes from 10 to roughly 100,000 enrolled students. We show that, like organisms, ecosystems and cities, universities and colleges scale in a surprisingly systematic fashion following simple power-law behavior. Comparing seven commonly accepted sectors of higher education organizations, we find distinct regimes of scaling between a school's total enrollment and its expenditures, revenues, graduation rates and economic added value. Our results quantify how each sector leverages specific economies of scale to address distinct priorities. Taken together, the scaling of features within a sector along with the shifts in scaling across sectors implies that there are generic mechanisms and constraints shared by all sectors, which lead to tradeoffs between their different societal functions and roles. We highlight the strong complementarity between public and private research universities, and community and state colleges, that all display superlinear returns to scale. In contrast to the scaling of biological systems, our results highlight that much of the observed scaling behavior is modulated by the particular strategies of organizations rather than an immutable set of constraints.

Comment on Alley, S.J., et al. As the Pandemic Progresses, How Does Willingness to Vaccinate against COVID-19 Evolve? Int. J. Environ. Res. Public Health 2021, 18, 797.

We would like to extend on the article by Alley et al [...].

COVID-19 heralds a new epistemology of science for the public good.

COVID-19 has revealed that science needs to learn how to better deal with the irreducible uncertainty that comes with global systemic risks as well as with the social responsibility of science towards the public good. Further developing the epistemological principles of new theories and experimental practices, alternative investigative pathways and communication, and diverse voices can be an important contribution of history and philosophy of science and of science studies to ongoing transformations of the scientific enterprise.

Quantifying simultaneous innovations in evolutionary medicine.

To what extent do simultaneous innovations occur and are independently from each other? In this paper we use a novel persistent keyword framework to systematically identify innovations in a large corpus containing academic papers in evolutionary medicine between 2007 and 2011. We examine whether innovative papers occurring simultaneously are independent from each other by evaluating the citation and co-authorship information gathered from the corpus metadata. We find that 19 out of 22 simultaneous innovative papers do, in fact, occur independently from each other. In particular, co-authors of simultaneous innovative papers are no more geographically concentrated than the co-authors of similar non-innovative papers in the field. Our result suggests producing innovative work draws from a collective knowledge pool, rather than from knowledge circulating in distinct localized collaboration networks. Therefore, new ideas can appear at multiple locations and with geographically dispersed co-authorship networks. Our findings support the perspective that simultaneous innovations are the outcome of collective behavior.

Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19.

Although the first coronavirus disease 2019 (COVID-19) wave has peaked with the second wave underway, the world is still struggling to manage potential systemic risks and unpredictability of the pandemic. A particular challenge is the "superspreading" of the virus, which starts abruptly, is difficult to predict, and can quickly escalate into medical and socio-economic emergencies that contribute to long-lasting crises challenging our current ways of life. In these uncertain times, organizations and societies worldwide are faced with the need to develop appropriate strategies and intervention portfolios that require fast understanding of the complex interdependencies in our world and rapid, flexible action to contain the spread of the virus as quickly as possible, thus preventing further disastrous consequences of the pandemic. We integrate perspectives from systems sciences, epidemiology, biology, social networks, and organizational research in the context of the superspreading phenomenon to understand the complex system of COVID-19 pandemic and develop suggestions for interventions aimed at rapid responses.

Cell phenotypes as macrostates of the GRN dynamics.

The two most fundamental processes describing change in biology, development, and evolution, occur over drastically different timescales. Development involves temporal sequences of cell states controlled by hierarchies of regulatory structures. It occurs over the lifetime of a single individual and is associated with gene expression level change of a given genotype. Evolution, by contrast, entails genotypic change through mutation, the acquisition/loss of genes and changes in the network topology of interactions among genes. It involves the emergence of new, environmentally selected phenotypes over the lifetimes of many individuals. We start by reviewing the most limiting aspects of the theoretical modeling of gene regulatory networks (GRNs) which prevent the study of both timescales in a common, mathematical language. We then consider the simple framework of Boolean network models of GRNs and point out its inadequacy to include evolutionary processes. As opposed to one-to-one maps to specific attractors, we adopt a many-to-one map which makes each phenotype correspond to multiple attractors. This definition no longer requires a fixed size for the genotype and opens the possibility for modeling the phenotypic change of a genotype, which is itself changing over evolutionary timescales. At the same time, we show that this generalized framework does not interfere with established numerical techniques for the identification of the kernel of controlling nodes responsible for cell differentiation through external signals.

From systems to biology: A computational analysis of the research articles on systems biology from 1992 to 2013.

Systems biology is a discipline that studies biological systems from a holistic and interdisciplinary perspective. It brings together biologists, mathematicians, computer scientists, physicists, and engineers, so it has both biology-oriented components and systems-oriented components. We applied several computational tools to analyze the bibliographic information of published articles in systems biology to answer the question: Did the research topics of systems biology become more biology-oriented or more systems-oriented from 1992 to 2013? We analyzed the metadata of 9923 articles on systems biology from the Web of Science database. We identified the most highly cited 330 references using computational tools and through close reading we divided them into nine categories of research types in systems biology. Interestingly, we found that articles in one category, namely, systems biology's applications in medical research, increased tremendously. This finding was corroborated by computational analysis of the abstracts, which also suggested that the percentages of topics on vaccines, diseases, drugs and cancers increased over time. In addition, we analyzed the institutional backgrounds of the corresponding authors of those 9923 articles and identified the most highly cited 330 authors over time. We found that before the mid-1990s, systems-oriented scientists had made the most referenced contributions. However, in recent years, researchers from biology-oriented institutions not only represented a huge percentage of the total number of researchers, but also had made the most referenced contributions. Notably, interdisciplinary institutions only produced a small percentage of researchers, but had made disproportionate contributions to this field.

Toward a mechanistic explanation of phenotypic evolution: The need for a theory of theory integration.

Reconciling different underlying ontologies and explanatory contexts has been one of the main challenges and impediments for theory integration in biology. Here, we analyze the challenge of developing an inclusive and integrative theory of phenotypic evolution as an example for the broader challenge of developing a theory of theory integration within the life sciences and suggest a number of necessary formal steps toward the resolution of often incompatible (hidden) assumptions. Theory integration in biology requires a better formal understanding of the structure of biological theories The strategy for integrating theories crucially depends on the relationships of the underlying ontologies.

Quantitative Perspectives on Fifty Years of the Journal of the History of Biology.

Journal of the History of Biology provides a fifty-year long record for examining the evolution of the history of biology as a scholarly discipline. In this paper, we present a new dataset and preliminary quantitative analysis of the thematic content of JHB from the perspectives of geography, organisms, and thematic fields. The geographic diversity of authors whose work appears in JHB has increased steadily since 1968, but the geographic coverage of the content of JHB articles remains strongly lopsided toward the United States, United Kingdom, and western Europe and has diversified much less dramatically over time. The taxonomic diversity of organisms discussed in JHB increased steadily between 1968 and the late 1990s but declined in later years, mirroring broader patterns of diversification previously reported in the biomedical research literature. Finally, we used a combination of topic modeling and nonlinear dimensionality reduction techniques to develop a model of multi-article fields within JHB. We found evidence for directional changes in the representation of fields on multiple scales. The diversity of JHB with regard to the representation of thematic fields has increased overall, with most of that diversification occurring in recent years. Drawing on the dataset generated in the course of this analysis, as well as web services in the emerging digital history and philosophy of science ecosystem, we have developed an interactive web platform for exploring the content of JHB, and we provide a brief overview of the platform in this article. As a whole, the data and analyses presented here provide a starting-place for further critical reflection on the evolution of the history of biology over the past half-century.

The diversity of experimental organisms in biomedical research may be influenced by biomedical funding.

Contrary to concerns of some critics, we present evidence that biomedical research is not dominated by a small handful of model organisms. An exhaustive analysis of research literature suggests that the diversity of experimental organisms in biomedical research has increased substantially since 1975. There has been a longstanding worry that organism-centric funding policies can lead to biases in experimental organism choice, and thus negatively impact the direction of research and the interpretation of results. Critics have argued that a focus on model organisms has unduly constrained the diversity of experimental organisms. The availability of large electronic databases of scientific literature, combined with interest in quantitative methods among philosophers of science, presents new opportunities for data-driven investigations into organism choice in biomedical research. The diversity of organisms used in NIH-funded research may be considerably lower than in the broader biomedical sciences, and may be subject to greater constraints on organism choice.

The origin and evolution of cell types.

Cell types are the basic building blocks of multicellular organisms and are extensively diversified in animals. Despite recent advances in characterizing cell types, classification schemes remain ambiguous. We propose an evolutionary definition of a cell type that allows cell types to be delineated and compared within and between species. Key to cell type identity are evolutionary changes in the 'core regulatory complex' (CoRC) of transcription factors, that make emergent sister cell types distinct, enable their independent evolution and regulate cell type-specific traits termed apomeres. We discuss the distinction between developmental and evolutionary lineages, and present a roadmap for future research.

The relativity of biological function.

Function is a central concept in biological theories and explanations. Yet discussions about function are often based on a narrow understanding of biological systems and processes, such as idealized molecular systems or simple evolutionary, i.e., selective, dynamics. Conflicting conceptions of function continue to be used in the scientific literature to support certain claims, for instance about the fraction of "functional DNA" in the human genome. Here we argue that all biologically meaningful interpretations of function are necessarily context dependent. This implies that they derive their meaning as well as their range of applicability only within a specific theoretical and measurement context. We use this framework to shed light on the current debate about functional DNA and argue that without considering explicitly the theoretical and measurement contexts all attempts to integrate biological theories are prone to fail.

Extended evolution: A conceptual framework for integrating regulatory networks and niche construction.

This paper introduces a conceptual framework for the evolution of complex systems based on the integration of regulatory network and niche construction theories. It is designed to apply equally to cases of biological, social and cultural evolution. Within the conceptual framework we focus especially on the transformation of complex networks through the linked processes of externalization and internalization of causal factors between regulatory networks and their corresponding niches and argue that these are an important part of evolutionary explanations. This conceptual framework extends previous evolutionary models and focuses on several challenges, such as the path-dependent nature of evolutionary change, the dynamics of evolutionary innovation and the expansion of inheritance systems.

Computational perspectives in the history of science: to the memory of Peter Damerow.

Computational methods and perspectives can transform the history of science by enabling the pursuit of novel types of questions, dramatically expanding the scale of analysis (geographically and temporally), and offering novel forms of publication that greatly enhance access and transparency. This essay presents a brief summary of a computational research system for the history of science, discussing its implications for research, education, and publication practices and its connections to the open-access movement and similar transformations in the natural and social sciences that emphasize big data. It also argues that computational approaches help to reconnect the history of science to individual scientific disciplines.